In photographic printing of film, such as a color photographic negative, it is a well-known practice to ‘correct’ the color balance by causing the overall color balance of the print to be a shade near gray. This correction strategy is based on the assumption that the overall average of the scene integrates to a gray color and is very effective at reducing the effects resulting from different scene illuminants that are spectrally different such as tungsten and daylight. In a like manner, image sensing apparatus such as a video camera averages color difference signals, R-Y and B-Y, over a time period to a zero value. This is equivalent to integrating to gray.
These methods work well for the majority of scene and illuminant combinations. However, when the scene subject matter is highly colored, particularly with a single dominant color, the ‘integrate to gray’ strategy fails as this dominant scene color is mistaken for an illuminant bias. This failure, known as subject failure, produces unpleasant colored casts in the color complementary to the dominant scene color. There are various strategies for minimizing these failures. These strategies are typically based on reducing the amount of correction based on a population of images and/or on the information in neighboring frames. Other color problems result from fading of the dyes in a photographic image, printing and processing errors, film keeping problems, and in the case of a color digital image that is captured directly with an electronic camera, an improperly adjusted black or white point in the camera.
With a digital color image obtained either directly from an electronic camera or indirectly by scanning a photographic print or film, it is possible to manually adjust the color balance by using any of the well known digital photographic manipulation tools, such as those provided by Adobe Photoshop®. However, manual adjustment is not practical for automated printing of digital images. A digital image provides much information that can be used for calculating color adjustments, and several methods have been proposed for performing these adjustments automatically. Some of these methods, such as that taught in U.S. Pat. No. 5,555,022 issued Sep. 10, 1996 to Haruki et al., divide the scene information into a plurality of regions or blocks representing different locations within a scene. Means to select and weight the correction of these blocks are then employed to provide automatic white balancing and to restrict the degree to which color correction gain is applied. U.S. Pat. No. 4,984,071 issued Jan. 8, 1991 to Yonezawa teaches a method for adjusting a gradation curve based on identifying shadow and highlight points by utilizing histogram data. U.S. Pat. No. 5,062,058 issued Oct. 29, 1991 to Morikawa describes a system that uses a cumulative histogram to designate highlight and shadow points. Other histogram based methods are taught by U.S. Pat. No. 5,812,286 issued Sep. 22, 1998 to Lin, and U.S. Pat. No. 5,265,200 issued Nov. 23, 1993 to Edgar.
While adjusting the gain and offset of individual color channels based on black and white points determined by the above mentioned methods significantly improves many digital color images, greatly biased digital color images, such as those that result from scanning severely underexposed films and color prints or transparencies that have suffered image quality loss owing to dye fade, oftentimes require non-linear adjustment of individual color channels. In the above mentioned U.S. Pat. No. 5,265,200, Edgar describes a method performing a second order best fit regression of the histogram data and includes methods to eliminate some histogram values from consideration in the regression. Edgar assumes, that by having eliminated some image pixels, that the shapes of individual color histograms, while possibly different, can be described by second order polynomials. Unfortunately, many histograms from digital color images are not well described, even with aggressive pixel rejection, by a second order best regression between channel code value level and frequency of occurrence of code value.
Another channel independent histogram method taught by Lin in the aforementioned U.S. Pat. No. 5,812,286, features independent color channel non-linear tone scale adjustment based on an additional parameter, such as the channel median. This allows a channel independent shaping of the color tone scale by a single parameter curve such as a gamma curve.
Another approach that combines color correction with tone scale corrections is based on random sampling within a digitized image and subsequently modifying the resulting histogram of these samples. Commonly assigned U.S. Pat. No. 4,677,465 issued Jun. 30, 1987 to Alkofer, and commonly assigned U.S. Pat. No. 4,729,016 issued Mar. 1, 1988 to Alkofer, disclose relatively complex methods that utilize these samples in a plurality of segmented contrast intervals through normalization techniques and with comparison to image population data.
Many of the above cited channel independent histogram methods only utilize a fraction of the image data near the ends of the histogram data. Furthermore, even in the cases where more data from individual color channel histograms are used, individual color channel histograms do not maintain any linkage of channel information at the pixel level. In other words, unless the image is monochrome, the pixels for a particular level in the histogram for one channel correspond to many levels in the other channels. This lack of linkage can cause errors with the above described histogram methods, particularly when there are highly saturated colors and when there is clipping of the image data. Thus there is a need therefore for an improved digital image processing method for automatically adjusting the color of a digital color image.